The invention discloses a long and large downhill road section driving speed control method based on a depth Q network, and the method comprises the following steps: building an environment model interacting with the depth Q network based on a long and large downhill road section scene, and defining a condition space, an action space and a reward function; constructing a deep Q network model, carrying out iterative interaction with the environment model to obtain a data set, and calculating to obtain a predicted Q value and a target Q value; constructing a loss function based on the predicted Q value, the target Q value and a reward function, and optimizing the loss function by using a gradient descent mode to realize training of a deep Q network model; and after multiple iterations, based on the trained deep Q network model, realizing control of the long and large downhill driving speed. The method is mainly based on data driving, comprehensively uses the driving safety, high efficiency and comfort indexes to represent the total income brought by speed control, is beneficial for avoiding bad driving behaviors in the driving process of the long and large downhill road section, and improves the traffic safety level.
本发明公开了一种基于深度Q网络的长大下坡路段行车速度控制方法,包括如下步骤:基于长大下坡路段场景,构建与深度Q网络交互的环境模型,定义状况空间、动作空间和奖励函数;构建深度Q网络模型,并与环境模型迭代交互,得到数据集,同时计算获得预测Q值和目标Q值;基于预测Q值、目标Q值和奖励函数,构建损失函数,并使用梯度下降方式对其进行优化,实现深度Q网络模型的训练;多次迭代后,基于训练后的深度Q网络模型实现对长大下坡行车速度的控制。本发明以数据驱动为主,综合使用行驶安全性、高效性和舒适性指标表征速度控制带来的总体收益,有益于规避长大下坡路段行车过程中的不良驾驶行为,提升交通安全水平。
Long and large downhill road section driving speed control method based on depth Q network
一种基于深度Q网络的长大下坡路段行车速度控制方法
2025-01-03
Patent
Electronic Resource
Chinese
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